MétaCan
Menu
Back to cohort
Record W2615720957 · doi:10.3138/cpp.2016-040

Embodied Emissions in Inputs and Outputs: A Value-Added Approach to National Emissions Accounting

2017· article· en· W2615720957 on OpenAlexaffvenueabout
G. Kent Fellows, Sarah Dobson

Bibliographic record

VenueCanadian Public Policy · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGreenhouse gasInput/outputEmbodied cognitionConsumption (sociology)Production (economics)National accountsValue (mathematics)Input–output modelEconomicsNatural resource economicsEnvironmental economicsResource (disambiguation)Resource consumptionAccounting methodEnvironmental scienceEconometricsAccountingMicroeconomicsComputer scienceMacroeconomicsMathematicsStatistics

Abstract

fetched live from OpenAlex

We present a consumption-based greenhouse gas (GHG) accounting model for Canada, based on a multiregional input–output formulation. The outputs resulting from this model comprise a set of detailed input–output tables displaying trade in embodied emissions across regions and sectors (in contrast to the usual financial value input–output tables). After a complete exposition of our embodied GHG emission accounting model, we provide a brief analysis of production- and consumption-based GHG emission footprints and the related interregional trade in embodied emissions across Canadian provinces for 2004–2011. Our initial analysis is intended to present the model and illustrate general conclusions to highlight the utility of the resulting detailed GHG input–output tables as a resource for future research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.378

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.285
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2017
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Public PolicySame topicEnvironmental Impact and SustainabilityFrench-language works237,207